Exploring Non-Linear Programming Formulations in QuantumCircuitOpt for Optimal Circuit Design

Given the limitations of current hardware, the theoretical gains promised by quantum computing remain unrealized across practical applications. But the gap between theory and hardware is closing, assisted by developments in quantum algorithmic modeling. One such recent development is QuantumCircuitOpt (QCOpt), an open-source software framework that leverages state-of-the-art optimization-based solvers to find provably optimal compact circuit decompositions, which are exact up to global phase and machine precision. The quantum circuit design problem can be modeled using non-linear, non-convex constraints. However, QCOpt reformulates these non-linear constraints using well-known linearization techniques such that the resulting design problem is solved as a Mixed-Integer Linear Programming (MILP) model. In this work, we instead explore whether the QCOpt could also be effective with a continuous Non-Linear Programming (NLP) model obtained via relaxation of the integer variables in the non-linear constraints. We are able to present not only multiple significant enhancements to QCOpt, with up to 11.3x speed-up in run times on average, but also opportunities for more generally exploring the behavior of gradient-based NLP solvers

A Programmable True Random Number Generator Using Commercial Quantum Computers

Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes such as those arising from quantum mechanics phenomena. We demonstrate that a quantum computer can serve as a high-quality, weakly random source for a generalized user-defined probability mass function (PMF). Specifically, QC measurement implements the process of variate sampling according to a user-specified PMF resulting in a word comprised of electronic bits that can then be processed by an extractor function to address inaccuracies due to non-ideal quantum gate operations and other system biases. We introduce an automated and flexible method for implementing a TRNG as a programmed quantum circuit that executes on commercially-available, gate-model quantum computers. The user specifies the desired word size as the number of qubits and a definition of the desired PMF. Based upon the user specification of the PMF, our compilation tool automatically synthesizes the desired TRNG as a structural OpenQASM file containing native gate operations that are optimized to reduce the circuit's quantum depth. The resulting TRNG provides multiple bits of randomness for each execution/measurement cycle; thus, the number of random bits produced in each execution is limited only by the size of the QC. We provide experimental results to illustrate the viability of this approach.Comment: 15 pages, 7 figures, SPIE Defense + Commercial Sensing: Quantum Information Science, Sensing, and Computation X

Automated Quantum Oracle Synthesis with a Minimal Number of Qubits

Several prominent quantum computing algorithms--including Grover's search algorithm and Shor's algorithm for finding the prime factorization of an integer--employ subcircuits termed 'oracles' that embed a specific instance of a mathematical function into a corresponding bijective function that is then realized as a quantum circuit representation. Designing oracles, and particularly, designing them to be optimized for a particular use case, can be a non-trivial task. For example, the challenge of implementing quantum circuits in the current era of NISQ-based quantum computers generally dictates that they should be designed with a minimal number of qubits, as larger qubit counts increase the likelihood that computations will fail due to one or more of the qubits decohering. However, some quantum circuits require that function domain values be preserved, which can preclude using the minimal number of qubits in the oracle circuit. Thus, quantum oracles must be designed with a particular application in mind. In this work, we present two methods for automatic quantum oracle synthesis. One of these methods uses a minimal number of qubits, while the other preserves the function domain values while also minimizing the overall required number of qubits. For each method, we describe known quantum circuit use cases, and illustrate implementation using an automated quantum compilation and optimization tool to synthesize oracles for a set of benchmark functions; we can then compare the methods with metrics including required qubit count and quantum circuit complexity.Comment: 18 pages, 10 figures, SPIE Defense + Commercial Sensing: Quantum Information Science, Sensing, and Computation X

Automated Synthesis of Quantum Subcircuits

The quantum computer has become contemporary reality, with the first two-qubit machine of mere decades ago transforming into cloud-accessible devices with tens, hundreds, or--in a few cases--even thousands of qubits. While such hardware is noisy and still relatively small, the increasing number of operable qubits raises another challenge: how to develop the now-sizeable quantum circuits executable on these machines. Preparing circuits manually for specifications of any meaningful size is at best tedious and at worst impossible, creating a need for automation. This article describes an automated quantum-software toolkit for synthesis, compilation, and optimization, which transforms classically-specified, irreversible functions to both technology-independent and technology-dependent quantum circuits. We also describe and analyze the toolkit's application to three situations--quantum read-only memories, quantum random number generators, and quantum oracles--and illustrate the toolkit's start-to-finish features from the input of classical functions to the output of quantum circuits ready-to-run on commercial hardware. Furthermore, we illustrate how the toolkit enables research beyond circuit synthesis, including comparison of synthesis and optimization methods and deeper understanding of even well-studied quantum algorithms. As quantum hardware continues to develop, such quantum circuit toolkits will play a critical role in realizing its potential.Comment: 49 pages, 25 figures, 20 table

Therapeutic Potential of Ralimetinib, a p38/MAPK14 Inhibitor, in Anaplastic Thyroid Cancer

View full abstracthttps://openworks.mdanderson.org/leading-edge/1028/thumbnail.jp

Characterization of Altered immunity in Anaplastic Thyroid Cancer

View full abstracthttps://openworks.mdanderson.org/leading-edge/1033/thumbnail.jp

The Differentiation and Stress Response Factor XBP-1 Drives Multiple Myeloma Pathogenesis

Multiple myeloma (MM) evolves from a highly prevalent premalignant condition termed MGUS. The factors underlying the malignant transformation of MGUS are unknown. We report a MGUS/MM phenotype in transgenic mice with Eμ-directed expression of the XBP-1 spliced isoform (XBP-1s), a factor governing unfolded protein/ER stress response and plasma-cell development. Eμ-XBP-1s elicited elevated serum Ig and skin alterations. With age, Eμ-xbp-1s transgenics develop features diagnostic of human MM, including bone lytic lesions and subendothelial Ig deposition. Furthermore, transcriptional profiles of Eμ-xbp-1s lymphoid and MM cells show aberrant expression of known human MM dysregulated genes. The similarities of this model with the human disease, coupled with documented frequent XBP-1s overexpression in human MM, serve to implicate XBP-1s dysregulation in MM pathogenesis

The inner junction protein CFAP20 functions in motile and non-motile cilia and is critical for vision

Motile and non-motile cilia are associated with mutually-exclusive genetic disorders. Motile cilia propel sperm or extracellular fluids, and their dysfunction causes primary ciliary dyskinesia. Non-motile cilia serve as sensory/signalling antennae on most cell types, and their disruption causes single-organ ciliopathies such as retinopathies or multi-system syndromes. CFAP20 is a ciliopathy candidate known to modulate motile cilia in unicellular eukaryotes. We demonstrate that in zebrafish, cfap20 is required for motile cilia function, and in C. elegans, CFAP-20 maintains the structural integrity of non-motile cilia inner junctions, influencing sensory-dependent signalling and development. Human patients and zebrafish with CFAP20 mutations both exhibit retinal dystrophy. Hence, CFAP20 functions within a structural/functional hub centered on the inner junction that is shared between motile and non-motile cilia, and is distinct from other ciliopathy-associated domains or macromolecular complexes. Our findings suggest an uncharacterised pathomechanism for retinal dystrophy, and potentially for motile and non-motile ciliopathies in general.</p