Low power cryogenic RF ASICs for quantum computing
David J. Frank, Sudipto Chakraborty, Kevin Tien, Pat Rosno, Mark Yeck, Joseph Glick, R. P. Robertazzi, Ray Richetta, John F. Bulzacchelli, Daniel Ramirez, Dereje Yilma, Andrew Davies, Rajiv Joshi, Scott Lekuch, Ken Inoue, Devin Underwood, Dorothy Wisnieff, Christian W. Baks, John Timmerwilke, Peilin Song, Blake R. Johnson, B. Gaucher, Daniel J. Friedman
Quantum computing is a promising new approach to computing that may allow the solution of certain problems that are very difficult to solve on classical computers. The field received its biggest impetus in 1994 when Shor [1] devised a quantum algorithm for finding the prime factors of large numbers and showed that this algorithm could greatly surpass the performance of known classical algorithms. Since that time, many additional applications have been proposed, and much effort has been devoted to developing hardware capable of running quantum algorithms.
Quantum computing is a promising new approach to computing that may allow the solution of certain problems that are very difficult to solve on classical computers. The field received its biggest impetus in 1994 when Shor [1] devised a quantum algorithm for finding the prime factors of large numbers and showed that this algorithm could greatly surpass the performance of known classical algorithms. Since that time, many additional applications have been proposed, and much effort has been devoted to developing hardware capable of running quantum algorithms.
Cryogenic electronics will have a strong impact on our society through applications as Quantum Computing but also, space communication, and high performance computing Quantum computers, for instance, have the potential to radically advance our computational capability and are predicted to strongly impact fields such as medicine, chemistry, science and finance by allowing to solve computational problems that cannot readily be solved by classical computers The hardware implementations of quantum computers rely on various quantum bit (qubit) technologies, such as superconducting qubits, spin qubits and Majorana fermions All of these Qubits require cryogenic temperatures (<4K) to operate efficiently, and need, and restitute Analog-RF signals for their manipulation, and results respectively Thus, there is a need for cryogenic electronics with a large array of functionalities, operating under extremely low noise conditions with limited power budgets Achieving this will require enhanced understanding of existing transistor technologies, 3D integrated systems and novel nanoelectronic devices employing unique low-temperature effects With these new devices, new ultra-low noise, ultra-low power, and wide-band circuits and systems are emerging, preparing the next computing revolution In this Workshop we will explore state of art status of Quantum computing applications and their associated technology and circuits analog-RF platforms
Quantum computing represents a revolutionary leap in computational power, leveraging the principles of quantum mechanics to process information in fundamentally new ways. Unlike classical computers, which use bits to represent data as 0s or 1s, quantum computers use quantum bits or qubits, enabling the phenomena of superposition and entanglement to perform complex calculations more efficiently (Nielsen & Chuang, 2010). This article explores the basic principles of quantum computing, the different models and algorithms that have been developed, and the current state of quantum hardware. The power of quantum computing lies in its ability to solve certain types of problems exponentially faster than classical computers. For instance, Shor's algorithm can factorize large integers in polynomial time, posing a significant threat to classical encryption methods (Shor, 1997). Similarly, Grover's algorithm provides a quadratic speedup for unstructured search problems, showcasing the potential for dramatic improvements in database search capabilities (Grover, 1996). These algorithms exemplify how quantum computing can revolutionize fields that rely on complex computations. Quantum hardware, however, is still in its nascent stage, with significant challenges to be overcome. Current quantum computers, such as those developed by IBM and Google, have demonstrated quantum supremacy but are limited by issues of qubit coherence and error rates (Arute et al., 2019). Quantum error correction and the development of more stable qubit technologies are critical areas of ongoing research (Preskill, 2018). Despite these challenges, recent advancements in quantum hardware and algorithms continue to drive the field forward, promising transformative impacts on industries such as cryptography, drug discovery, and artificial intelligence. As quantum computing continues to advance, it holds the promise of solving problems that are currently intractable for classical computers, paving the way for unprecedented technological and scientific advancements (Shor, 1997; Grover, 1996). This article provides a comprehensive overview of the current landscape of quantum computing, discussing its foundational principles, key algorithms, hardware challenges, and potential applications. By understanding these aspects, we can better appreciate the profound implications of quantum computing for the future of technology and science.
Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.
Factoring large integers has been one of the most difficult problems in the history of mathematics and computer science. There was no efficient solution of this problem until Shor's algorithm emerged. Shor's algorithm is a polynomial time factoring algorithm which works on a quantum computer. Quantum computing is a new paradigm of computing that uses quantum mechanical phenomena in solving problems. The computers we are using right now are called classical computer. The most efficient classical factoring algorithm is General Number Field Sieve (GNFS). GNFS also cannot factor integers in polynomial time. In this paper, we compared these two algorithms in factoring integer in a standalone system.
Quantum computation is a completely new and different paradigm for how to store and process information. It offers the possibility of exponential computational advantage for certain types of hard problems, but the hardware for implementing quantum algorithms is still at an early stage. The basic idea of quantum computing is to replace the ordinary binary bits of conventional computing with their quantum equivalent, or qubit. A qubit can be any quantum system whose states can be prepared and controlled, provided that we can then fabricate them in large quantities and then couple them to one another in prescribed ways. While there are several candidate technologies for building a quantum computer, one of the most promising is superconducting quantum circuits, operated at cryogenic temperatures approaching 0.01 K.
The security of messages encoded via the widely used RSA public key encryption system rests on the enormous computational effort required to find the prime factors of a large number N using classical (conventional) computers. In 1994 Peter Shor showed that for sufficiently large N, a quantum computer could perform the factoring with much less computational effort. This paper endeavors to explain, in a fashion comprehensible to the nonexpert, the RSA encryption protocol; the various quantum computer manipulations constituting the Shor algorithm; how the Shor algorithm performs the factoring; and the precise sense in which a quantum computer employing Shor’s algorithm can be said to accomplish the factoring of very large numbers with less computational effort than a classical computer. It is made apparent that factoring N generally requires many successive runs of the algorithm. Our analysis reveals that the probability of achieving a successful factorization on a single run is about twice as large as commonly quoted in the literature.