Jesse Ames’ Post

The latest article by @globalqi ‘s @quantum-computing-report: https://lnkd.in/g4WEPZH8 discusses efforts toward putting order in the complex pool of algorithms and learning if there are relationships, which reveal which algorithm primitives might lead to successful quantum advantage. We also describe another group’s work to apply a three-layer abstraction hierarchy for quantum programming, which mimics that abstraction which HPC users are familiar. Enjoy! #QuantumInComing

A really good read on the subject of Quantum Computing and Coding. For those who haven't delved into the subject, this is an excellent primer. #quantumcomputing #coding #thefuture An Introduction to Quantum Computers and Quantum Coding | by Oliver W. Johnson | Aug, 2024 | Towards Data Science

Exploring the future of computation; a Grover's Algorithm Grover's algorithm is a quantum search algorithm that can significantly speed up the process of finding a specific item in an unsorted database. Unlike classical search algorithms, which typically require searching through each item one by one, Grover's algorithm leverages the principles of quantum mechanics to achieve a quadratic speedup. How it works: State Preparation: The algorithm starts by preparing a quantum state that is a superposition of all possible states in the database. This means that the quantum computer is simultaneously considering every possible solution. Oracle Application: An oracle, a quantum operation that identifies the desired item, is applied to the quantum state. This operation marks the desired solution with a phase shift. Amplification: The algorithm then amplifies the amplitude of the desired solution while reducing the amplitudes of the other states. This is done using a quantum operation called Grover diffusion. Measurement: Finally, the quantum state is measured. With high probability, the measurement will yield the desired solution. Advantages: Quadratic Speedup: Grover's algorithm can find an item in an unsorted database much faster than any classical algorithm. Applications: It has potential applications in various fields, including database search, cryptography, and machine learning. By harnessing the power of quantum mechanics, Grover's algorithm provides a promising approach to solving search problems more efficiently. Notice that with this Qiskit we can only simulate the quantum state, which allows us to explore the events of the algorithm. #futureprogramming #quantum #research #programming #computerscience

An alternative paradigm. We all know that binary, 0s and 1s, is the basis of today's computing but ever hear of ternary i.e., 0, 1, 2? Here’s the twist: Ternary systems have the ability to store more information in a digit, and computations can be more efficient that way. Pack more data into fewer digits, even use less power in some cases. But there's a catch; Binary circuits are easy, they are just on/off states, but a ternary system would need to distinguish three different states. That just makes the hardware design that much more complicated, error prone, and adds a whole new level of problems in differentiating voltages in a reliable manner. The binary system won out due to its simplicity, and reliability despite early experiments like the Setun computer in the 1950s. Not to mention, the whole digital world is based on binary and to switch to ternary would require a complete overhaul of everything from processors to programming languages. However, as we expand the frontiers of technology, will ternary computation have a niche in the future. Someday quantum computing or specialized architectures could make this system viable again. Would you embrace a ternary-driven world? :) (Whenever I teach Binary Search to my students, I tend to add an extra topic that is 'Ternary Search' and 'N-ary Search' which is not usually been taught. The purpose is to encourage them to keep exploring.) #techinnovation #computing #futureoftech #binary #ternarysystems #hardwaredesign

I recently read an interesting post about how to make quantum computers easier to program. The main topic was an innovative model proposed by MIT researchers, called a Quantum Control Machine, which aims to simplify the programming process. Quantum computing is a revolutionary technology, but it comes with challenges. The main one is programming, mainly due to the rules quantum computers follow that differ from the classical computer rules. This makes it hard for people, even programmers, to get into the field and makes it harder for companies to develop and maintain products using quantum technologies. I really like the idea a more programmer-friendly way of using quantum computers. This would make it easier for developers to learn and would help them work more quickly. It would also help us do things that we can't do with classical computers.

The calculation of the norm of vectors is essential in both artificial intelligence and quantum computing for tasks such as feature scaling, regularization, distance metrics, convergence criteria, representing quantum states, ensuring unitarity of operations, error correction, and designing quantum algorithms and circuits. DZone

How can we streamline quantum algorithm development across rapidly evolving hardware platforms? Anastasia Marchenkova examines this challenge and potential solutions in Classiq's latest blog post. She explores how modern development environments could help researchers focus more on innovation and less on rewriting boilerplate code. It's an interesting look at removing bottlenecks in quantum computing research. Read the full post: https://lnkd.in/dbCcUwZ5 #QuantumComputing #OpenSource #ResearchTools

Joint work with Dhrumil Patel and Patrick Coles now published in Quantum - the open journal for quantum science: https://lnkd.in/g7vUGDjn Popular Summary: Many real-world problems in science and industry can be expressed as optimization problems, which involve finding the best solution while meeting specific constraints. Among these, a special class of optimization problems called semidefinite programming holds significance. They are widely used to model or approximate problems arising in various fields such as operations research, combinatorial optimization, control theory, and quantum information theory. For solving these programs, quantum algorithms have been proven to provide a quadratic speedup over classical algorithms. However, these quantum algorithms are not well-suited for current quantum devices, which are noisy and limited in their capabilities. In this work, we propose three quantum algorithms designed to run on these noisy devices. Our algorithms are hybrid quantum-classical algorithms that have a classical computer available for optimization, only calling a quantum computer for tasks that are not efficiently solvable by it. We rigorously analyze the performance of one of our algorithms, quantifying how rapidly it converges to the optimal value. Finally, to demonstrate their practicality, we numerically simulate our quantum algorithms for problems like MaxCut, a prominent graph theoretic problem. Our simulations showcase their effectiveness even in the presence of noise.

A quantum computer could solve problems that are intractable on a classical computer — making hard tasks like factoring easy. But, a quantum computer can be much harder to program than a classical one. MIT researchers highlight why, and how a new conceptual model for a quantum computer could bring the quantum advantage closer to reach. To tackle this problem, CSAIL researchers outline a new conceptual model for a quantum computer, called a “quantum control machine,” that could bring us closer to making programs as easy to write as those for regular classical computers. 👉 Quantum Control Machine: The Limits of Control Flow in Quantum Programming Charles Yuan, Agnes Villanyi, Michael Carbin Abstract Quantum algorithms for tasks such as factorization, search, and simulation rely on control flow such as branching and iteration that depends on the value of data in superposition. High-level programming abstractions for control flow, such as switches, loops, and higher-order functions, are ubiquitous in classical languages. By contrast, many quantum languages do not provide high-level abstractions for control flow in superposition, and instead require the use of hardware-level logic gates to implement such control flow. The reason for this gap is that whereas a classical computer supports control flow using a program counter that can depend on data, the typical architecture of a quantum computer does not provide a program counter that can depend on data in superposition. As a result, the complete set of control flow abstractions that can be correctly realized on a quantum computer has not yet been established. In this work, we provide a complete characterization of the properties of control flow abstractions that are correctly realizable on a quantum computer. First, we prove that even on a quantum computer whose program counter exists in superposition, one cannot correctly realize control flow in quantum algorithms by lifting the classical conditional jump instruction to work in superposition. This theorem denies the ability to directly lift general abstractions for control flow such as the λ-calculus from classical to quantum programming. In response, we present the necessary and sufficient conditions for control flow to be correctly realizable on a quantum computer. We introduce the quantum control machine, an instruction set architecture featuring a conditional jump that is restricted to satisfy these conditions. We show how this design enables a developer to correctly express control flow in quantum algorithms using a program counter in place of logic gates. 👉 https://lnkd.in/dbXcnQUi #machinelearning

Toby Cubitt, #CTO of Phasecraft discusses the current state of quantum computing, likening it to the early, fundamental-science-driven days of biotech startups. He contends that the main challenge for practical quantum-enabled applications lies not in developing platforms or programming languages but in creating applications and algorithms for quantum computers. Toby believes that high-value applications currently within reach are quantum simulations, which can dramatically advance R&D in materials science and chemistry, but require a deep understanding of current-day hardware. https://lnkd.in/eTsCh6X9