55 research outputs found
A Study of Optimal 4-bit Reversible Toffoli Circuits and Their Synthesis
Optimal synthesis of reversible functions is a non-trivial problem. One of
the major limiting factors in computing such circuits is the sheer number of
reversible functions. Even restricting synthesis to 4-bit reversible functions
results in a huge search space (16! {\approx} 2^{44} functions). The output of
such a search alone, counting only the space required to list Toffoli gates for
every function, would require over 100 terabytes of storage. In this paper, we
present two algorithms: one, that synthesizes an optimal circuit for any 4-bit
reversible specification, and another that synthesizes all optimal
implementations. We employ several techniques to make the problem tractable. We
report results from several experiments, including synthesis of all optimal
4-bit permutations, synthesis of random 4-bit permutations, optimal synthesis
of all 4-bit linear reversible circuits, synthesis of existing benchmark
functions; we compose a list of the hardest permutations to synthesize, and
show distribution of optimal circuits. We further illustrate that our proposed
approach may be extended to accommodate physical constraints via reporting
LNN-optimal reversible circuits. Our results have important implications in the
design and optimization of reversible and quantum circuits, testing circuit
synthesis heuristics, and performing experiments in the area of quantum
information processing.Comment: arXiv admin note: substantial text overlap with arXiv:1003.191
Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging
The controlled-SWAP and controlled-controlled-NOT gates are at the heart of
the original proposal of reversible classical computation by Fredkin and
Toffoli. Their widespread use in quantum computation, both in the
implementation of classical logic subroutines of quantum algorithms and in
quantum schemes with no direct classical counterparts, have made it imperative
early on to pursue their efficient decomposition in terms of the lower-level
gate sets native to different physical platforms. Here, we add to this body of
literature by providing several logically equivalent CNOT-count-optimal
circuits for the Toffoli and Fredkin gates under all-to-all and linear qubit
connectivity, the latter with two different routings for control and target
qubits. We then demonstrate how these decompositions can be employed on
near-term quantum computers to mitigate coherent errors via equivalent circuit
averaging. We also consider the case where the three qubits on which the
Toffoli or Fredkin gates act nontrivially are not adjacent, proposing a novel
scheme to reorder them that saves one CNOT for every SWAP. This scheme also
finds use in the shallow implementation of long-range CNOTs. Our results
highlight the importance of considering different entanglement structures and
connectivity constraints when designing efficient quantum circuits.Comment: Main text: 10 pages, 8 figures. Appendix: 4 sections, 5 figures. QASM
files will be made available in open-source online platform upon next update
of preprin
Quantum Simulation Logic, Oracles, and the Quantum Advantage
Query complexity is a common tool for comparing quantum and classical
computation, and it has produced many examples of how quantum algorithms differ
from classical ones. Here we investigate in detail the role that oracles play
for the advantage of quantum algorithms. We do so by using a simulation
framework, Quantum Simulation Logic (QSL), to construct oracles and algorithms
that solve some problems with the same success probability and number of
queries as the quantum algorithms. The framework can be simulated using only
classical resources at a constant overhead as compared to the quantum resources
used in quantum computation. Our results clarify the assumptions made and the
conditions needed when using quantum oracles. Using the same assumptions on
oracles within the simulation framework we show that for some specific
algorithms, like the Deutsch-Jozsa and Simon's algorithms, there simply is no
advantage in terms of query complexity. This does not detract from the fact
that quantum query complexity provides examples of how a quantum computer can
be expected to behave, which in turn has proved useful for finding new quantum
algorithms outside of the oracle paradigm, where the most prominent example is
Shor's algorithm for integer factorization.Comment: 48 pages, 46 figure
Quantum Computation
In the last few years, theoretical study of quantum systems serving as
computational devices has achieved tremendous progress. We now have strong
theoretical evidence that quantum computers, if built, might be used as a
dramatically powerful computational tool. This review is about to tell the
story of theoretical quantum computation. I left out the developing topic of
experimental realizations of the model, and neglected other closely related
topics which are quantum information and quantum communication. As a result of
narrowing the scope of this paper, I hope it has gained the benefit of being an
almost self contained introduction to the exciting field of quantum
computation.
The review begins with background on theoretical computer science, Turing
machines and Boolean circuits. In light of these models, I define quantum
computers, and discuss the issue of universal quantum gates. Quantum
algorithms, including Shor's factorization algorithm and Grover's algorithm for
searching databases, are explained. I will devote much attention to
understanding what the origins of the quantum computational power are, and what
the limits of this power are. Finally, I describe the recent theoretical
results which show that quantum computers maintain their complexity power even
in the presence of noise, inaccuracies and finite precision. I tried to put all
results in their context, asking what the implications to other issues in
computer science and physics are. In the end of this review I make these
connections explicit, discussing the possible implications of quantum
computation on fundamental physical questions, such as the transition from
quantum to classical physics.Comment: 77 pages, figures included in the ps file. To appear in: Annual
Reviews of Computational Physics, ed. Dietrich Stauffer, World Scientific,
vol VI, 1998. The paper can be down loaded also from
http://www.math.ias.edu/~doria
New Data Structures and Algorithms for Logic Synthesis and Verification
The strong interaction between Electronic Design Automation (EDA) tools and Complementary Metal-Oxide Semiconductor (CMOS) technology contributed substantially to the advancement of modern digital electronics. The continuous downscaling of CMOS Field Effect Transistor (FET) dimensions enabled the semiconductor industry to fabricate digital systems with higher circuit density at reduced costs. To keep pace with technology, EDA tools are challenged to handle both digital designs with growing functionality and device models of increasing complexity. Nevertheless, whereas the downscaling of CMOS technology is requiring more complex physical design models, the logic abstraction of a transistor as a switch has not changed even with the introduction of 3D FinFET technology. As a consequence, modern EDA tools are fine tuned for CMOS technology and the underlying design methodologies are based on CMOS logic primitives, i.e., negative unate logic functions. While it is clear that CMOS logic primitives will be the ultimate building blocks for digital systems in the next ten years, no evidence is provided that CMOS logic primitives are also the optimal basis for EDA software. In EDA, the efficiency of methods and tools is measured by different metrics such as (i) the result quality, for example the performance of a digital circuit, (ii) the runtime and (iii) the memory footprint on the host computer. With the aim to optimize these metrics, the accordance to a specific logic model is no longer important. Indeed, the key to the success of an EDA technique is the expressive power of the logic primitives handling and solving the problem, which determines the capability to reach better metrics. In this thesis, we investigate new logic primitives for electronic design automation tools. We improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. We develop synthesis tools exploiting the majority and biconditional expressiveness. Our tools show strong results as compared to state-of-the-art academic and commercial synthesis tools. Indeed, we produce the best results for several public benchmarks. On top of the enhanced synthesis power, our methods are the natural and native logic abstraction for circuit design in emerging nanotechnologies, where majority and biconditional logic are the primitive gates for physical implementation. We accelerate formal methods by (i) studying properties of logic circuits and (ii) developing new frameworks for logic reasoning engines. We prove non-trivial dualities for the property checking problem in logic circuits. Our findings enable sensible speed-ups in solving circuit satisfiability. We develop an alternative Boolean satisfiability framework based on majority functions. We prove that the general problem is still intractable but we show practical restrictions that can be solved efficiently. Finally, we focus on reversible logic where we propose a new equivalence checking approach. We exploit the invertibility of computation and the functionality of reversible gates in the formulation of the problem. This enables one order of magnitude speed up, as compared to the state-of-the-art solution. We argue that new approaches to solve EDA problems are necessary, as we have reached a point of technology where keeping pace with design goals is tougher than ever
QUANTUM COMPUTING AND HPC TECHNIQUES FOR SOLVING MICRORHEOLOGY AND DIMENSIONALITY REDUCTION PROBLEMS
Tesis doctoral en perÃodo de exposición públicaDoctorado en Informática (RD99/11)(8908
Quantum Algorithmic Techniques for Fault-Tolerant Quantum Computers
Quantum computers have the potential to push the limits of computation in areas such as quantum chemistry, cryptography, optimization, and machine learning. Even though many quantum algorithms show asymptotic improvement compared to classical ones, the overhead of running quantum computers limits when quantum computing becomes useful. Thus, by optimizing components of quantum algorithms, we can bring the regime of quantum advantage closer. My work focuses on developing efficient subroutines for quantum computation. I focus specifically on algorithms for scalable, fault-tolerant quantum computers. While it is possible that even noisy quantum computers can outperform classical ones for specific tasks, high-depth and therefore fault-tolerance is likely required for most applications. In this thesis, I introduce three sets of techniques that can be used by themselves or as subroutines in other algorithms.
The first components are coherent versions of classical sort and shuffle. We require that a quantum shuffle prepares a uniform superposition over all permutations of a sequence. The quantum sort is used within the shuffle and as well as in the next algorithm in this thesis. The quantum shuffle is an essential part of state preparation for quantum chemistry computation in first quantization.
Second, I review the progress of Hamiltonian simulations and give a new algorithm for simulating time-dependent Hamiltonians. This algorithm scales polylogarithmic in the inverse error, and the query complexity does not depend on the derivatives of the Hamiltonian. A time-dependent Hamiltonian simulation was recently used for interaction picture simulation with applications to quantum chemistry.
Next, I present a fully quantum Boltzmann machine. I show that our algorithm can train on quantum data and learn a classical description of quantum states. This type of machine learning can be used for tomography, Hamiltonian learning, and approximate quantum cloning
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