74 research outputs found
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
A CQM-based approach to solving a combinatorial problem with applications in drug design
The use of D-Wave's Leap Hybrid solver is demonstrated here in solving a
Knapsack optimization problem: finding meal combinations from a fixed menu that
fit a diner's constraints. This is done by first formulating the optimization
problem as a Constrained Quadratic Model (CQM) and then submitting it to a
quantum annealer. We highlight here the steps needed, as well as the
implemented code, and provide solutions from a Chicken and Waffle restaurant
menu. Additionally, we discuss how this model may be generalized to find
optimal drug molecules within a large search space with many complex, and often
contradictory, structures and property constraints.Comment: 10 pages, 2 table
The lower energy consumption in cryptocurrency mining processes by SHA-256 Quantum circuit design used in hybrid computing domains
Cryptocurrency mining processes always lead to a high energy consumption at
considerably high production cost, which is nearly one-third of cryptocurrency
(e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256
cryptographic hashing function, by using the alternative quantum computers,
hybrid quantum computers or more larger quantum computing devices like quantum
annealers, it would be possible to reduce the mining energy consumption with a
quantum hardware's low-energy-operation characteristics. Within this work we
demonstrated the use of optimized quantum mining facilities which would replace
the classical SHA-256 and high energy consuming classical hardware in near
future
Scaling Advantage in Approximate Optimization with Quantum Annealing
Quantum annealing is a heuristic optimization algorithm that exploits quantum
evolution to approximately find lowest energy states. Quantum annealers have
scaled up in recent years to tackle increasingly larger and more highly
connected discrete optimization and quantum simulation problems. Nevertheless,
despite numerous attempts, a computational quantum advantage in exact
optimization using quantum annealing hardware has so far remained elusive.
Here, we present evidence for a quantum annealing scaling advantage in
approximate optimization. The advantage is relative to the top classical
heuristic algorithm: parallel tempering with isoenergetic cluster moves
(PT-ICM). The setting is a family of 2D spin-glass problems with high-precision
spin-spin interactions. To achieve this advantage, we implement quantum
annealing correction (QAC): an embedding of a bit-flip error-correcting code
with energy penalties that leverages the properties of the D-Wave Advantage
quantum annealer to yield over 1,300 error-suppressed logical qubits on a
degree-5 interaction graph. We generate random spin-glass instances on this
graph and benchmark their time-to-epsilon, a generalization of the
time-to-solution metric for low-energy states. We demonstrate that with QAC,
quantum annealing exhibits a scaling advantage over PT-ICM at sampling low
energy states with an optimality gap of at least 1.0%. This amounts to the
first demonstration of an algorithmic quantum speedup in approximate
optimization.Comment: 11 pages, 6 figure
Quantum Advantage for All
We show that the algorithmic complexity of any classical algorithm written in
a Turing-complete programming language polynomially bounds the number of
quantum bits that are required to run and even symbolically execute the
algorithm on a quantum computer. In particular, we show that any classical
algorithm that runs in time and
space requires no more than quantum bits to
execute, even symbolically, on a quantum computer. With
for all , the
quantum bits required to execute may therefore not exceed
and may come down to if memory
consumption by is bounded by a constant. Our construction works by encoding
symbolic execution of machine code in a finite state machine over the
satisfiability-modulo-theory (SMT) of bitvectors, for modeling CPU registers,
and arrays of bitvectors, for modeling main memory. The FSM is linear in the
size of the code, independent of execution time and space, and represents the
reachable machine states for any given input. The FSM may be explored by
bounded model checkers using SMT and SAT solvers as backend. However, for the
purpose of this paper, we focus on quantum computing by unrolling and
bit-blasting the FSM into (1)~satisfiability-preserving quadratic unconstrained
binary optimization (QUBO) models targeting adiabatic forms of quantum
computing such as quantum annealing, and (2)~semantics-preserving quantum
circuits (QCs) targeting gate-model quantum computers. With our compact QUBOs,
real quantum annealers can now execute simple but real code even symbolically,
yet only with potential but no guarantee for exponential speedup, and with our
QCs as oracles, Grover's algorithm applies to symbolic execution of arbitrary
code, guaranteeing at least in theory a quadratic speedup
Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer
In materials informatics, searching for chemical materials with desired
properties is challenging due to the vastness of the chemical space. Moreover,
the high cost of evaluating properties necessitates a search with a few clues.
In practice, there is also a demand for proposing compositions that are easily
synthesizable. In the real world, such as in the exploration of chemical
materials, it is common to encounter problems targeting black-box objective
functions where formalizing the objective function in explicit form is
challenging, and the evaluation cost is high. In recent research, a Bayesian
optimization method has been proposed to formulate the quadratic unconstrained
binary optimization (QUBO) problem as a surrogate model for black-box objective
functions with discrete variables. Regarding this method, studies have been
conducted using the D-Wave quantum annealer to optimize the acquisition
function, which is based on the surrogate model and determines the next
exploration point for the black-box objective function. In this paper, we
address optimizing a black-box objective function containing discrete variables
in the context of actual chemical material exploration. In this optimization
problem, we demonstrate results obtaining parameters of the acquisition
function by sampling from a probability distribution with variance can explore
the solution space more extensively than in the case of no variance. As a
result, we found combinations of substituents in compositions with the desired
properties, which could only be discovered when we set an appropriate variance.Comment: 14pages, 4figures, 4table
Multilevel Combinatorial Optimization Across Quantum Architectures
Emerging quantum processors provide an opportunity to explore new approaches
for solving traditional problems in the post Moore's law supercomputing era.
However, the limited number of qubits makes it infeasible to tackle massive
real-world datasets directly in the near future, leading to new challenges in
utilizing these quantum processors for practical purposes. Hybrid
quantum-classical algorithms that leverage both quantum and classical types of
devices are considered as one of the main strategies to apply quantum computing
to large-scale problems. In this paper, we advocate the use of multilevel
frameworks for combinatorial optimization as a promising general paradigm for
designing hybrid quantum-classical algorithms. In order to demonstrate this
approach, we apply this method to two well-known combinatorial optimization
problems, namely, the Graph Partitioning Problem, and the Community Detection
Problem. We develop hybrid multilevel solvers with quantum local search on
D-Wave's quantum annealer and IBM's gate-model based quantum processor. We
carry out experiments on graphs that are orders of magnitudes larger than the
current quantum hardware size, and we observe results comparable to
state-of-the-art solvers in terms of quality of the solution
Highly accurate quantum optimization algorithm for CT image reconstructions based on sinogram patterns
Computed tomography (CT) has been developed as a non-destructive technique
for observing minute internal images of samples. It has been difficult to
obtain photo-realistic (clean or clear) CT images due to various unwanted
artifacts generated during the CT scanning process, along with limitations of
back projection algorithms. Recently, an iterative optimization algorithm has
been developed that uses the entire sinogram to reduce errors caused by
artifacts. In this paper, we introduce a new quantum algorithm for
reconstructing CT images. This algorithm can be used with any type of light
source as long as the projection is defined. Suppose we have an experimental
sinogram produced by a Radon transform. To find the CT image of this sinogram,
we express the CT image as a combination of qubits. After the Radon transform
of the undetermined CT image, we find the combination of the actual sinogram
and the optimized qubits. The global energy optimization value used here can
determine the value of qubits through a gate model quantum computer or quantum
annealer. In particular, the new algorithm can also be used for cone-beam CT
image reconstructions and will be of great help in the field of medical
imaging.Comment: 16 Pages, 5 figures, 1 Tabl
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