Konaploinks
20 hours ago
NEWBIE HOMEWORK
Me. …Every investor should read this as many times as necessary until you get it. Then you can relax and go to the beach. Go snorkeling at Kahaluu State Beach Park with the turtles there. Sip on a Maitai while having lunch at Huggo’s Then have dinner at Kai eats and drinks while watching the Kona sunset sink beneath Kailua Bay . Keep your eye on IonQ.
IonQ is building universal quantum computers based on trapped ion technology. This is an easy enough sentence to read, but a much harder one to understand! What are universal quantum computers, anyway? What are trapped ion quantum computers? How can you evaluate ours against others? What will they be used for?
We hope this primer will help you begin to answer some of these basic questions about quantum computers, and leave you better-prepared to understand and further investigate this world-changing technology.
How quantum computers compute
The easiest way to explain quantum computers is to use classical computers — the kind you’re reading this on — as a reference point. First, a potentially-surprising statement: even though classical computing has given us the most spectacular wave of technological innovation in human history, there are certain problems that it will never be able to solve.
It’s not an issue of power, it’s an issue of how classical computers do their calculations in the first place — even if Moore’s Law continues for another thousand years, they will still be no closer to modeling complex chemical interactions, optimizing delivery routes, and a variety of other problems.
Many of these problems have a similar quality: combinatorial explosion, where there are many different variables that all have to be weighed against each other to find an optimal solution. The time (or memory) needed to solve these grow exponentially as the number of variables increase, and this exponential explosion can rapidly overwhelm today's computers, which have to either guess-and-check every possible combination in turn (which could take billions of years), or else resort to imperfect and expensive approximations.
Quantum computers, on the other hand, have access to an entirely different kind of computational system which has the ability to solve these kinds of problems in far fewer steps, without having to try every solution sequentially. That computational system is quantum mechanics, the complex math that describes how atoms and other tiny particles behave and interact.
In a classical computer, we use bits, normally represented by small amounts of electrical current and combined via special gates (normally created with transistors) to perform calculations. Quantum computers have analogues to both of these concepts: classical bits become quantum bits, or qubits for short, and classical logic gates become quantum logic gates, which allow us to take advantage of quantum systems’ most powerful (and unintuitive) properties: superposition and entanglement.
We’ll start with the qubits. In a classical computer, a bit can either be a zero or a one; these are the only two options. Thanks to superposition, qubits can be a zero, a one, or a complex combination of both. You might hear this described as “zero and one at the same time.” This is a useful way to think about what’s going on, but it’s not quite right. It’s also not quite wrong. Superposition just doesn’t have a direct analogue in the non-quantum world; and it’s hard to explain exactly why without a longer explanation about quantum physics — one too long for this primer to cover. The important part is that it makes qubits far more powerful in their ability to store and calculate information than classical bits.
To make many qubits work together to solve a problem, we have to entangle them, using a different kind of quantum logic gate. Once entangled, the two qubits can no longer be described independently; in fact, for every additional qubit you entangle, you need an exponentially-increasing amount of information to completely describe the state of the system. This isn’t the same as saying entanglement allows you to store or process an exponential amount of computationally-useful information; again, it’s a little more complicated than that, and the reason why involves a long trip into the emergent field known as quantum information theory.
What entanglement does allow is the creation of immense computational power — power that dramatically outstrips classical computers — with relatively few qubits. With even 60 or so high-quality qubits, you could run certain algorithms in minutes that would take the world’s largest supercomputer billions of years.
There are many ways to make, control, and entangle a qubit. The most commonly-known of these is the “superconducting” approach, where synthetic qubits are made using solid-state fabrication, similar to classical computer chips, cooled to extremely low temperatures, and controlled via microwave pulses. In recent months, advances using this method from companies like Google, IBM and others have grabbed headlines.
We use a different approach, using actual atoms as qubits. Our atomic qubits are ionized, trapped in 3D space by electromagnetic forces, and manipulated and entangled via lasers. We believe it offers several distinct advantages to solid-state and other less-well-known approaches, such as neutral atoms and spin dot qubits, and has the ability to be the lowest-error, most-powerful quantum computing platform in the market.
Judging the power of quantum computers
By now, you’ve hopefully got a sense of what a quantum computer is, what it does, and how many different ways there are to make one. Because these approaches are so different, comparing the power and ability of these systems can be challenging.
One common method for evaluating the power of quantum computers is to count qubits. Qubits are at the heart of every quantum computer, and so it seems like a reasonable approach, but it’s not actually very effective in giving a complete picture of those qubits’ ability to perform meaningful calculations.
Imagine comparing railroads solely on the number of miles of track they own. There are a number of other factors, such as where the railroad track runs, the freight volume, the level of interconnectedness and how much of the track is in good repair. Depending on the topology, it wouldn’t take many closed bridges in key locations to split the rail system in two, or even bring the whole operation to a standstill.
The same is true with quantum computers. To realistically judge a quantum computer’s power, we have to ask other questions about the qubits and the system as a whole; about quality, connectivity, coherence, and more. The complete list of possible questions is long, but we think these five get at most of the important information:
1. What is the qubits’ coherence time?
Coherence time is a measurement of how long a qubit can maintain its complex quantum state — essentially, a qubit’s lifespan. When a qubit is set up in some quantum mechanical state and left alone, how long before that state decays?
If we were able to keep a qubit perfectly isolated from its surrounding environment, it could theoretically hold its state forever. But, in practice, even the slightest perturbation will collapse this delicate quantum state and ruin the computation entirely. It’s a matter of when, not if, and how long it takes is directly related to how well-isolated the qubit is. In a trapped-ion system, like IonQ’s, coherence time is usually measured in seconds to minutes; in solid state systems, it’s microseconds to milliseconds.
Coherence time matters for quantum computation not only as a way to understand how well-isolated the qubits are, but also as the total budget for computation. You have to complete all of your quantum operations before the qubits decohere and lose their information, so the longer the coherence time, the greater the capacity for long, complex algorithms, and the more valuable the computer is.
2. What is the qubits’ connectivity?
Connectivity in this case means the qubits’ ability to “talk” to each other via an entangling gate. In trapped ion systems, we have what’s called “complete” connectivity: any pair of qubits can make a gate in a single operation. In other technologies, such as superconducting quantum computers, only physically adjacent qubits can do so without using other qubits as intermediaries, introducing error and overhead that can reduce accuracy and computational power.
3. How identical are your qubits?
Qubits must be as identical as possible. When scaling a quantum computer past a trivial number of qubits, building reliable interactions between them becomes enormously difficult if they aren’t. If their resonant frequencies (or anything else!) are even slightly different, the calibration and tuning of each qubit and the way it interacts with every other qubit in the system quickly becomes a nightmare. In solid-state systems, even the slightest error in manufacturing one of their synthetic atoms can create immense issues. The trapped ion approach, however, uses actual atoms, making them inherently perfect and perfectly identical.
4. What is the gate fidelity?
Gate fidelity determines how many gates you can run in the first place, and that determines the size of the algorithms you can run. Like classical computers, logic gates are the basic building blocks of an algorithm. But unlike classical computers, quantum gates aren’t perfect yet, and the errors add up fast.
You’ll sometimes see these errors reported directly, as gate error rate, or you may see a metric called fidelity, which is the inverse of the error rate — a 1% gate error rate equals 99% fidelity, 0.5% error means 99.5% fidelity, and so-on. They answer the same question: when you perform a gate, how close is the end state to your desired state?
Error Rate
2 x 2
2 x 2 x 2 x 2 x 2 x 2 x 2
0%
4
512
.1%
4 +- .004
512 +- 4
5%
4 +- .20
512 +- 172
20%
4 +- .80
512 +- 426
Critically, these errors compound as the size of the algorithm grows and more gates are performed (as seen above). As error rate increases, the depth of calculation becomes limited, and it’s not long before you have a nearly useless answer. It’s great to have lots of qubits, but if they have poor gate fidelity, your quantum computer isn’t very powerful. As you add qubits, you must improve gate fidelity to make it useful.
You might have heard about an error corrected qubit or logical qubit. These zero-error qubits are created by combining a number of physical qubits into a single "logical" qubit — how many depends on the fidelity of these physical qubits, so fidelity is still important — using complex error-correction algorithms. While these are likely the future of quantum computation, no one has been able to create one in hardware yet. Many great minds in academia, industry, and within IonQ itself are working on achieving this next great milestone.
5. How many qubits are there?
Finally, we can count qubits. Only when qubits are identical, with good coherence times and gate fidelities, does adding qubits make a quantum computer more powerful. In fact, it exponentially increases the computational power — every time you add a qubit, the computational system doubles in size. This allows us, in a sense, to extend Moore’s Law by adding only a single qubit a year. At IonQ, our goal is to double the number of qubits every year, producing a doubly-exponential growth in computing power.
Put together, this leads to a Quantum Goldilocks Rule:
A large number of qubits isn’t useful if the qubits are of low fidelity (and limited gate depth).
A small number of qubits with high fidelity isn’t useful either.
A quantum computer that has a sufficient fidelity to allow at least n x n gates (where n is the number of qubits) is just right.
Game-changing use cases
Once you’ve evaluated those five factors to judge the power of a quantum computer, it’s time to begin to explore the applications that make the most sense to run on it.
The reality is, few systems are powerful enough to address real-world user applications in a way that offers a quantum speedup at all, let alone a commercially meaningful one. But, hardware is progressing fast, and this won’t be true for much longer. The time to find the game-changing applications is now, and many companies have already begun. In 2019, numerous leading companies across a variety of industries began examining highly-complex real-world problems that might be solved with quantum computers. With the availability of quantum computing on the cloud, we believe this number — and the applications they find — will skyrocket.
As quantum computers become more accessible and more powerful, there will be a cultural shift as companies start to envision tackling even more ambitious sets of problems:
Pharmaceutical companies will seek to discover new kinds of drugs, and be able to speed up early-stage development by being able to simulate much more complex compounds, therapies, and interactions.
The agriculture industry could save massive amounts of money—and reduce impact on the climate—by re-tooling the process used to create fertilizer.
Governments could better address climate change by figuring out more efficient ways to capture and remove carbon and other greenhouse gases from the atmosphere.
The fintech industry will leverage quantum technology to develop valuable portfolios based on vast collections of assets with interconnected dependencies. It could also be used to better detect fraud.
Logistics companies will be able to save tremendous amounts of money by optimizing the routes their drivers take. The average driver makes 120 deliveries a day, meaning the total possible combinations he or she takes is a number with 199 digits--larger than how old the Earth is in nanoseconds. It’s estimated the company could save $30 million by figuring out how a single driver can cover one less mile a day. Multiply that across its entire network, and the savings could be staggering.
Quantum hardware companies will even turn the technology inward, using quantum computers to look for better qubits and more efficient quantum algorithms.
We don’t know for certain if quantum computers will be able to handle all of these applications. But the potential is there—if not for these exact problems, then ones like them. And ones we cannot even begin to conceptualize.
Nor do we know precisely when these computers will deliver on their immense promise. But we know they’re going to have a huge impact, and we suspect it’ll be sooner than you might think. Quantum is the natural evolution of computing, and will allow us to address problems we’ve long had to ignore because we didn’t have the power.
Quantum computing is going to give companies new superpowers. It will be a true game changer for entire industries. Sure, we don’t exactly know what those superpowers will be, but even so, would you turn down the opportunity to find out?
[Published by IonQ Staff at IonQ.com on January 18th, 2024]
Konaploinks
2 days ago
Quantum Benchmarking
Understanding Algorithmic Qubits (#AQ)
A benchmark that measures what matters most: a system’s ability to successfully run your target quantum workloads
Talk To An Expert
Quantum Computers Are Complex, Predicting Their Value Doesn't Have To Be
#AQ is an application based benchmark, which aggregates performance across 6 widely known quantum algorithms that are relevant to the most promising near term quantum use cases: Optimization, Quantum Simulation and Quantum Machine Learning.
orange rectangleOptimization
Problems involving complex routing, sequencing and more
checkmarkAmplitude Estimation
checkmarkMonte Carlo Simulation
blue rectangleQuantum Simulation
Understand the nature of the very small
checkmarkHamiltonian Simulation
checkmarkVariational Quantum Eigensolver
grey rectangle
Quantum Machine Learning
Draw inferences from patterns in data, at scale
checkmarkQuantum Fourier Transform
checkmarkPhase Estimation
These Near Term Quantum Use Cases Are Widely Applicable To Multiple Industry Verticals*
Algorithmic Qubits Diagram
orange circleOptimization
blue circleQuantum Simulation
grey circleQuantum Machine Learning
5 - highest relevance, 1 - lowest relevance
*Based on algorithmic derivatives most commonly used for IonQ industry use cases
Putting #AQ Into Practice
IONQ System
#AQ 25
IonQ Aria
A Single Metric, A Wealth Of Information
A computer's #AQ can reveal how the system will perform against the workloads that are the most valuable to you. #AQ is a summary and analysis of multiple quantum algorithms. Here is what IonQ Aria's #AQ means, from a practical lens.
checkmark6 instances of the most valuable quantum algorithms were run on IonQ Aria
checkmark#AQ Algorithms of up to ~600 entangling gates were run successfully
checkmark#AQ Algorithms were successfully run on up to 25 qubits
checkmarkAlgorithm results were deemed successful if they acheived over 37% Worst Case results fidelity
Predicting Performance Against Your Intended Workloads
All of the information behind the #AQ benchmark can be summarized in a single chart, that provides insight into how a system performs for a particular class of algorithms. By identifying the algorithmic classes you intend to use the system for, you can make a direct prediction about the performance of an algorithm with a specific gate width and gate depth.
#AQ Benchmark on IonQ Aria (Merged) Sep 26, 2022
Translating #AQ to Real World Impact
#AQ 5 Translating AQ to Real World impact H2O
#AQ 4
Simulating H20
Water was simulated on IonQ Harmony, was running at #AQ 4, in 2020. The algorithm used 3 qubits across 3 parameters in the problem set and was able to produce accurate results.
Read Case Study
#AQ 20 Translating AQ to Real World impact Battery
#AQ 20
Simulating Li20
Lithium oxide, a chemical of interest in battery development, was simulated on IonQ Aria, running at #AQ 20, in 2022. The algorithm used 12 qubits across 72 parameters in the problem set and was able to produce accurate results.
Read Case Study
Exponential Growth: Put it into Perspective
A quantum computer’s computational space, represented by the possible qubit states outlined below, doubles every time a single qubit is added to the system. Because #AQ measures a system’s useful qubits, an increase of #AQ 1 represents a doubling of that system’s computational space.
As #AQ increases, the scale becomes hard to wrap your head around. Use the below buttons to compare two #AQ metrics and explore the difference in their computational space represented by the difference in scale between two familiar objects.
Compare The Scale Of The Computational Space
#AQ 1
#AQ 2
#AQ 5
#AQ 10
#AQ 16
#AQ 23
#AQ 30
#AQ 36
#AQ 45
#AQ 51
Use the buttons to compare two #AQ metrics
Width Of A Paper Clip
#AQ 1
Width Of A Paper Clip
2 Possible Encoded States
Width Of The Solar System
#AQ 51
Width Of The Solar System
~2 Quadrillion Possible Encoded States
Is ~1 Quadrillion Times
Smaller Than
Qubits vs. Algorithmic Qubits
Every #AQ Is Built With Qubits, But Not All Qubits Result In An #AQ
A system’s qubit count reveals information about the physical structure of the system but does not indicate the quality of the system, which is the largest indicator of utility. For a qubit to contribute to an algorithmic qubit it must be able to run enough gates to successful return useful results across the 6 algorithms in the #AQ definition. This is a high bar to pass and is the reason many system’s #AQ is significantly lower than its physical qubit count.
IonQ Benchmark Beliefs
At IonQ, We Believe Benchmarks Should:
Measure Real World Utility
For most quantum computing users a benchmark will only be as useful as its ability to predict how a quantum computer will perform on a task that has value for them. A focus on real world utility is at the heart of IonQ's approach.
Be Easily Understood
Benchmarks should be a communication tool that can clearly and easily convey information about a complex system. IonQ aims to provide simple benchmarks that aggregate information across a variety of practical measurements.
Test Critical Aspects Of Performance
Any benchmark used at IonQ is designed to measure the full quantum system, including the classical hardware stack, optimization tools, error mitigation techniques, and of course, the quantum gate and measurement operations. We believe this is the way to most accurately represent the performance our customers expect.
Be Easily Verifiable
Benchmarks are only valuable if the cost, time and classical compute resources required for validation are practical. We believe that a precisely defined benchmark, that anyone can run, will provide more utility than a resource intensive, theoretical proof of quantum advantage over classical compute.
Measuring Algorithmic Qubits (#AQ)
Step 1
Define and Run the Algorithms
In defining the #AQ metric, we derive significant inspiration from the recent benchmarking study from the QED-C. Just like the study, we start by defining benchmarks based on instances of several currently popular quantum algorithms.
Explore the full repository
Optimization
Problems involving complex routing, sequencing and more
checkmarkAmplitude Estimation
checkmarkMonte Carlo Simulation
Quantum Simulation
Understand the nature of the very small
checkmarkHamiltonian
checkmarkVariational Quantum Eigensolver
Quantum Machine Learning
Problems involving complex routing, sequencing and more
checkmarkQuantum Fourier Transform
checkmarkPhase Estimation
Step 2
Organize And Aggregate The Results
Building upon previous work on volumetric benchmarking, we then represent the success probability of the circuits corresponding to these algorithms as colored circles placed on a 2D plot whose axes are the 'depth' and the 'width' of the circuit corresponding to the algorithm instance.
Chart of Number of 2Q Gates
Step 3
Release Updated Versions Of #AQ
New benchmarking suites should be released regularly, and be identified with an #AQ version number. The #AQ for a particular quantum computer should reference this version number under which the #AQ was evaluated. Ideally, new versions should lead to #AQ values that are consistent with the existing set of benchmarks and not deviate drastically, but new benchmarks will cause differences, and that is the intention - representing the changing needs of customers.
Step Three
#AQ Version 1.0 Definition:
This repository defines circuits corresponding to instances of several quantum algorithms. The AQ.md document in the repository outlines the algorithms that must be run to calculate #AQ.
The circuits are compiled to a basis of CX, Rx, Ry, Rz to count the number of CX gates. For version 1.0, the transpiler in Qiskit version 0.34.2 must be used with these basis gates, with the seed_transpiler option set to 0, and no other options set.
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Konaploinks
2 days ago
Join us on Tuesday, December 3, 2024, at 10:00 am PT for a live webinar “IonQ’s Full-Stack Quantum Innovation.” Register today as spots are limited.
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IonQ Leadership
OUR MISSION:
To build the world’s best quantum computers to solve the world’s most complex problems.
Quantum computers are a revolutionizing technology — they have the potential to transform business, society, and the planet for the better, and IonQ is at the forefront of this revolution.
After over 25 years of academic research, IonQ was founded in 2015 by Chris Monroe and Jungsang Kim with $2 million in seed funding from New Enterprise Associates, a license to core technology from the University of Maryland and Duke University, and the goal of taking trapped ion quantum computing out of the lab and into the market. In the following three years, we raised an additional $20 million from GV, Amazon Web Services, and NEA, and built two of the world’s most accurate quantum computers.
In 2019, we raised another $55 million in a round led by Samsung and Mubadala, and announced partnerships with Microsoft and Amazon Web Services to make our quantum computers available via the cloud.
In 2020 and 2021, we built additional generations of high performance quantum hardware, added Google Cloud Marketplace to our cloud partner roster and announced a series of collaborations and business partnerships with leading academic and commercial institutions.
On October 1st, 2021, IonQ began trading as IONQ on the New York Stock Exchange, making it the world's first public pure-play quantum computing company. We remain hard at work realizing the world-changing potential of quantum computing.
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doc2016
3 days ago
gemini ai says it better than me......that's what it's for? lol:
"cuda-q and ionq quantum simulation to inference dna sequence with google alpha fold3 for syncon creation of dna/rna/protein treatment?
Show drafts
This is a really fascinating and complex question that explores the cutting edge of how quantum computing, AI, and biotechnology might converge!
Here's a breakdown of the different components and their potential interplay:
1. CUDA-Q and IonQ Quantum Simulation
CUDA-Q: Nvidia's CUDA-Q is a platform designed to accelerate quantum computing workloads. It provides tools and libraries for developers to write quantum algorithms and run them on GPUs and quantum processors, including those from IonQ.
IonQ Quantum Computers: IonQ builds trapped-ion quantum computers, which are a leading type of quantum computer with high qubit fidelity and long coherence times.
Quantum Simulation: Together, CUDA-Q and IonQ's quantum computers could be used to perform quantum simulations of biological molecules, such as DNA, RNA, and proteins. These simulations could provide insights into the behavior and interactions of these molecules at the atomic level.
2. Google AlphaFold3 and DNA Sequence Inference
AlphaFold3: Google's AlphaFold3 is a powerful AI model that can predict the 3D structure of proteins from their amino acid sequences. It has revolutionized protein folding prediction.
DNA Sequence Inference: While AlphaFold3 primarily focuses on proteins, it's conceivable that similar AI models could be developed or adapted to infer information about DNA or RNA sequences based on their structure or other properties. This could involve predicting gene function, identifying potential mutations, or understanding how DNA interacts with other molecules.
3. SynCon Creation of DNA/RNA/Protein Treatments
SynCon (Synthetic Consensus) Vaccine Design: SynCon is a vaccine design approach that involves creating a synthetic version of a virus's genetic sequence that represents a "consensus" among various strains, aiming to provide broader protection against different variants.
DNA/RNA/Protein Treatments: SynCon could potentially be extended beyond vaccines to design DNA, RNA, or protein-based treatments for various diseases. This could involve:
Gene Therapy: Using synthetic DNA or RNA to correct genetic defects or introduce therapeutic genes.
Protein Engineering: Designing synthetic proteins with specific functions, such as targeting cancer cells or delivering drugs.
Putting it all Together
Here's how these technologies might potentially work together:
Quantum Simulation: CUDA-Q and IonQ's quantum computers could simulate the interactions of DNA, RNA, or proteins with drug candidates or other molecules.
AI Inference: AlphaFold3 or similar AI models could analyze the simulation data to infer properties of the DNA/RNA sequences or predict the effects of different treatments.
SynCon Design: This information could then be used to guide the design of SynCon-based DNA/RNA/protein treatments, optimizing them for efficacy and safety.
Challenges and Considerations
Technology Maturity: Quantum computing and generative AI are still evolving fields. Integrating these technologies for drug discovery and development will require further advancements and overcoming challenges in scalability, accuracy, and interpretability.
Data Requirements: Training AI models and performing accurate quantum simulations require large and high-quality datasets.
Ethical Considerations: Developing and deploying these technologies responsibly and ethically is crucial.
Potential Benefits
Accelerated Drug Discovery: This combination of technologies could significantly accelerate the drug discovery process by providing a more efficient way to simulate and analyze molecular interactions.
Personalized Medicine: It could enable the development of personalized treatments tailored to an individual's genetic makeup and specific disease characteristics.
New Therapeutic Approaches: It could lead to new therapeutic approaches based on DNA, RNA, or protein-based therapies, potentially addressing diseases that are currently difficult to treat.
In Summary
The integration of CUDA-Q, IonQ quantum simulation, AlphaFold3, and SynCon represents a fascinating convergence of quantum computing, AI, and biotechnology. While still in its early stages, this combination of technologies holds immense potential for revolutionizing drug discovery and development, leading to more effective and personalized treatments for various diseases."
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