Practical Quantum Computing For Developers __HOT__
The first involves building capabilities to classically communicate and parallelize operations across multiple processors. This will open the avenue to a broader set of techniques necessary for practical quantum systems, such as improved error mitigation techniques and intelligent workload orchestration, by combining classical compute resources with quantum processors that can extend in size.
Practical Quantum Computing for Developers
IBM is home to some of the best cryptographic experts globally who have developed quantum-safe schemes that will be able to deliver practical solutions to this problem. Currently, IBM is working in close cooperation with its academic and industrial partners, as well as the U.S. National Institute of Standards and Technology (NIST), to bring these schemes to the forefront of data security technologies.
We offer a cloud-based, full-stack of systems, software, developer tools, and services to enable enterprises, government agencies, national laboratories, and academic organizations to build real applications using the power of quantum computing.
In this session, Alex Condello, Manager of Applications Development Technology and Tools at D-Wave, will talk about the growing quantum application development ecosystem and how developers can start learning to code on a quantum computer today. Alex will also explore some of the early applications that developers and companies have built to-date using D-Wave's system.
As a practical matter, their proposed system could withstand an error rate of 4.1%, which Thompson said is well within the realm of possibility for current quantum computers. In previous systems, the state-of-the-art error correction could handle less than 1% error, which Thompson said is at the edge of the capability of any current quantum system with a large number of qubits.
Although it's not technically difficult to generate such a signal, there are significant hurdles here when it comes to managing the many signals that would be needed for a practical quantum computer. For one, the signals sent to the different qubits would need to be synchronized at picosecond timescales. And you need some way to convey these different signals to the different qubits so as to be able to make them do different things. That's a big stumbling block.
In the current pre-prototyping phase of quantum computing, individual qubit control is still unavoidable: It's required to get the most out of the few qubits that we now have. Soon, though, as the number of qubits available increases, researchers will have to work out systems for multiplexing control signals and the measurements of the qubits.
To prepare for these developments, chip designers, chip-fabrication-process engineers, cryogenic-control specialists, experts in mass data handling, quantum-algorithm developers, and others will need to work together closely.
Such a complex collaboration would benefit from an international quantum-engineering road map. The various tasks required could then be assigned to the different sets of specialists involved, with the publishers of the road map managing communication between groups. By combining the efforts of academic institutions, research institutes, and commercial companies, we can and will succeed in building practical quantum computers, unleashing immense computing power for the future.
Create citation alert 2632-2153/3/1/015034 Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.
In this work, we develop a QVM platform named Qsun using the wavefunction approach towards the QML applications. In Qsun, a quantum register is represented by a wavefunction, and quantum gates are manipulated directly by updating the amplitude of the wavefunction. Measurement results rely on probabilities of the wavefunction. Our simple approach yields faster computation speed for a small number of qubit when compared to other QVMs such as Qiskit, ProjectQ, or Pennylane. Basing on this generic QVM, we aim to exploit the advantages of QDP with the parameter-shift rule as the core engine towards practical applications in QML. Two representative examples of QML are demonstrated: quantum linear regression (QLR) and QNN. These algorithms are compared to standard progams: Qiskit, ProjectQ, and Pennylane, and classical algorithms when applicable. In these comparisons, Qsun performs slightly better for QDP, QLR, and QNN. All in all, Qsun is an efficient combination of QVM with QDP features that is oriented toward machine learning problems. In the following, we introduce the QVM platform Qsun and its performance compared to others in section 2, followed by an introduction to the QDP implementation within Qsun in section 3. We then discuss some QML applications of the Qsun package in section 4.
Given a quantum state \psi(\vec\theta)\rangle with \vec\theta as variational parameters and an observable \widehat C, the task is to seek the global minimum of the expectation value C(\vec\theta) = \langle\psi(\vec\theta)\widehat C\psi(\vec\theta)\rangle with respect to parameters \vec\theta. For example, if \widehat C is a Hamiltonian, its global minimum is the ground state energy. In general, C(\vec\theta) is called as the cost function, and minimizing the cost function requires its derivative with respect to parameters \vec\theta, \partial C(\vec\theta) / \partial\theta. In classical computing, if the analytical form of \partial C(\vec\theta) / \partial\theta is unknown, finite difference methods are often used to evaluate the derivative approximately. Although this approximation is fast and easy to implement, its accuracy depends on discretization steps. In contrast to the classical finite differentiation, QDP is an automatic and exact method to compute the derivative of a function. QDP is thus essential for accurate gradient computation in multiple VQAs, including QML models.
KB: I think in the long term, quantum computing and communication will change how we deal with encoded information on the internet. In Google Chrome, in fact, you can already change your cryptography to a possible post-quantum cryptography setup.
KB: When I try to explain quantum computing to someone, if they know the physics or chemistry of quantum mechanics, then I can usually start there to explain how to do the computing side. And the other side is also true: if people understand computing pretty well, I can explain the extra modules that quantum computing gives you.
KB: We have some ideas. In a classic computer you work with voltage, but in quantum computing, I need to somehow carry information from one place to another. Do messenger qubits that carry information to other parts of the computer have to be the same type of qubit that the rest of the computer is made of? We're not sure yet.
Researchers have speculated about quantum computation for decades, but recent years have seen steady experimental advances, as well as theoretical proofs that it can efficiently do things that classical computing devices cannot. The field is attracting billions of dollars from governmental research agencies and technology giants, as well as startups. Conventional companies also are exploring the potential impact of quantum computing.
Despite this excitement, including successful sensing devices, quantum computing has not made practical contributions. Moreover, there is still no winner among very different schemes to physically implement quantum bits, or qubits. None of them is 'good enough' yet to achieve supercomputer-scale calculations, and they all face major barriers to low error rates and large device counts.
Even optimistically, it could take many years to realize large-scale, error-corrected quantum computing. In the interim, researchers and companies are seeking uses that can exploit the small, less-reliable systems that already are available.
All the qubit candidates face major engineering and technical challenges to scaling up, which is one reason none is yet the clear winner. Still, "Over the last decade, all quantum-computing technologies have reduced the error by an order of magnitude," Brown said. "I don't see any hard limits yet."
Although such deep skepticism is not widespread, quantum experiments do not yet demonstrate useful calculations with a performance or cost advantage over current supercomputers. "What size of device allows us to beat classical computers for problems of practical interest or scientific interest?" asked Brown. "We just don't know."
A prominent candidate application is calculating the electronic properties of molecules or solids. Indeed, quantum simulations of materials inspired physicist Richard Feynman's early proposal for quantum computing, and small molecules and exotic quantum phases have been successfully modeled.
Continued large national investments in quantum computing are motivated in part by concerns about falling behind other countries. For example, government security organizations worry that factorization capability could break secure communications. In their recent book, however, Garfinkel and his co-author, legal expert Chris Hoofnagle of the University of California at Berkeley, note that the symmetric AES encryption with 256 bits can evade any reasonable quantum machine. 041b061a72