Lifeboat Foundation

Army researchers developed a new way to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers want to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the Army, the new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields of the future.

Researchers at the Army Research Laboratory have developed a new method to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers aim to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the army, these new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields in the future.

Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.

MIT engineers develop a hybrid process that connects photonics with “artificial atoms,” to produce the largest quantum chip of its type.

Such noise nearly drowned out the signal in Google’s quantum supremacy experiment. Researchers began by setting the 53 qubits to encode all possible outputs, which ranged from zero to 2⁵³. They implemented a set of randomly chosen interactions among the qubits that in repeated trials made some outputs more likely than others. Given the complexity of the interactions, a supercomputer would need thousands of years to calculate the pattern of outputs, the researchers said. So by measuring it, the quantum computer did something that no ordinary computer could match. But the pattern was barely distinguishable from the random flipping of qubits caused by noise. “Their demonstration is 99% noise and only 1% signal,” Kuperberg says.

To realize their ultimate dreams, developers want qubits that are as reliable as the bits in an ordinary computer. “You want to have a qubit that stays coherent until you switch off the machine,” Neven says.

Scientists’ approach of spreading the information of one qubit—a “logical qubit”—among many physical ones traces its roots to the early days of ordinary computers in the 1950s. The bits of early computers consisted of vacuum tubes or mechanical relays, which were prone to flip unexpectedly. To overcome the problem, famed mathematician John von Neumann pioneered the field of error correction.

New insight into the spin behavior in an exotic state of matter puts us closer to next-generation spintronic devices.

Aside from the deep understanding of the natural world that quantum physics theory offers, scientists worldwide are working tirelessly to bring forth a technological revolution by leveraging this newfound knowledge in engineering applications. Spintronics is an emerging field that aims to surpass the limits of traditional electronics by using the spin of electrons, which can be roughly seen as their angular rotation, as a means to transmit information.

But the design of devices that can operate using spin is extremely challenging and requires the use of new materials in exotic states–even some that scientists do not fully understand and have not experimentally observed yet. In a recent study published in Nature Communications, scientists from the Department of Applied Physics at Tokyo University of Science, Japan, describe a newly synthesized compound with the formula KCu₆AlBiO₄(SO₄)₅Cl that may be key in understanding the elusive “quantum spin liquid (QSL)” state. Lead scientist Dr Masayoshi Fujihala explains his motivation: “Observation of a QSL state is one of the most important goals in condensed-matter physics as well as the development of new spintronic devices. However, the QSL state in two-dimensional (2D) systems has not been clearly observed in real materials owing to the presence of disorder or deviations from ideal models.”

A deep tech startup building cryptographic solutions to secure hardware, software, and communications systems for a future when quantum computers may render many current cybersecurity approaches useless is today emerging out of stealth mode with $7 million in funding and a mission to make cryptographic security something that cannot be hackable, even with the most sophisticated systems, by building systems today that will continue to be usable in a post-quantum future.

PQShield (PQ being short for “post-quantum”), a spin out from Oxford University, is being backed in a seed round led by Kindred Capital, with participation also Crane Venture Partners, Oxford Sciences Innovation and various angel investors, including Andre Crawford-Brunt, Deutsche Bank’s former global head of equities.

PQShield was founded in 2018, and its time in stealth has not been in vain.

Optical frequency synthesizers – systems that output laser beams at precise and stable frequencies – have proven extremely valuable in a variety of scientific endeavors, including space exploration, gas sensing, control of quantum systems, and high-precision light detection and ranging (LIDAR). While they provide unprecedented performance, the use of optical frequency synthesizers has largely been limited to laboratory settings due to the cost, size, and power requirements of their components. To reduce these obstacles to widespread use, DARPA launched the Direct On-Chip Digital Optical Synthesizer (DODOS) program in 2014. Key to the program is the miniaturization of necessary components and their integration into a compact module, enabling broader deployment of the technology while unlocking new applications.

To accomplish its goals, DODOS is leveraging advances in microresonators – tiny structures that store light in microchips – to produce optical frequency combs in compact integrated packages. Frequency combs earn their name by converting a single-color input laser beam into a sequence of many additional colors that are evenly spaced and resemble a hair comb. With a sufficiently wide array of comb “teeth,” innovative techniques to eliminate noise become possible that make combs an attractive option for systems needing precise frequency references.

Until recently, creating frequency combs from microresonators was a complex affair that required sophisticated control schemes, dedicated circuitry, and oftentimes, an expert scientist to carefully observe and fine-tune the operation. This is primarily due to the sensitive properties of the microresonator, which needs the perfect amount of light at a special operating frequency – or color – to be provided by an input laser in order for the comb to turn on and even then, producing a coherent or stable comb state could not be guaranteed every time.

A central challenge in developing quantum computers and long-range quantum networks is the distribution of entanglement across many individually controllable qubits¹. Colour centres in diamond have emerged as leading solid-state ‘artificial atom’ qubits^2,3 because they enable on-demand remote entanglement⁴, coherent control of over ten ancillae qubits with minute-long coherence times⁵ and memory-enhanced quantum communication⁶. A critical next step is to integrate large numbers of artificial atoms with photonic architectures to enable large-scale quantum information processing systems. So far, these efforts have been stymied by qubit inhomogeneities, low device yield and complex device requirements. Here we introduce a process for the high-yield heterogeneous integration of ‘quantum microchiplets’—diamond waveguide arrays containing highly coherent colour centres—on a photonic integrated circuit (PIC). We use this process to realize a 128-channel, defect-free array of germanium-vacancy and silicon-vacancy colour centres in an aluminium nitride PIC. Photoluminescence spectroscopy reveals long-term, stable and narrow average optical linewidths of 54 megahertz (146 megahertz) for germanium-vacancy (silicon-vacancy) emitters, close to the lifetime-limited linewidth of 32 megahertz (93 megahertz). We show that inhomogeneities of individual colour centre optical transitions can be compensated in situ by integrated tuning over 50 gigahertz without linewidth degradation. The ability to assemble large numbers of nearly indistinguishable and tunable artificial atoms into phase-stable PICs marks a key step towards multiplexed quantum repeaters^7,8 and general-purpose quantum processors^9,10,11,12.