Yapay zeka ve bilgisayar oyunları alanında öncü bir bilgisayar uzmanı ve IBM araştırmacısı Arthur Samuel, 1952 yılında makine öğrenimi terimini ilk kez kullanıp “Bilgisayarlara açıkça programlanmadan öğrenme imkanı veren çalışma alanı” olarak tanımlamıştır. Yine kendisinin 1952 yılında geliştirdiği ilk bilgisayarlı öğrenme programı, bu alanın oluşumuna katkı sunan ilk somut adım olarak görülmektedir. Samuel tarafından geliştirilen bu program, bir dama oyunu üzerinedir ve hangi stratejilerin kazandığını programa sunup, programın hareket stratejilerini sunulan verilere göre değerlendirmektedir.
Samuel tarafından 1959 yılında yayınlanan diğer bir çalışma olan “Some Studies in Machine Learning Using the Game of Checkers” adlı çalışmasında da makine öğrenimini ve yöntemlerini ortaya koymuştur. Bu çalışmadan sonra, makine öğrenimi yaklaşımından etkilenen birçok araştırmacı ve bilgisayar uzmanı halen daha heyecan verici gelişmelerin yaşandığı bu alan üzerine çalışmaya başlamıştır. Sonuç itibariyle, oldukça yoğun çalışmaların yürütüldüğü yapay zeka ve makine öğrenimi alanları günümüze kadar çok farklı yöntemler ve kazanımlar elde edip; işlevsel, faydalı ve etkili bir teknoloji halini almıştır. Bu teknolojilerden faydalanan sayısız bilimsel araştırmaya, iş modellerine ve sağlık sektöründeki kullanımlarıyla insan hayatına büyük katkılar sunmaktadır. Resmen gerçek bir değer!
Makine öğrenimi temel olarak, gerçek anlamda programlanmadan, yani herhangi bir insan yardımı olmadan bilgisayarların karar mekanizmalarını temel alarak öğrenme süreçleriyle birleştirip, otomatikleştirmek ve geliştirmek olarak açıklanabilir. Makine öğreniminde veri bilimi ve istatistiksel analiz yöntemleri kullanmaktadır. Öğrenme süreçleri, programı kaliteli verilerle beslemek ve daha sonra bu verileri ve farklı algoritmaları kullanarak makine öğrenme modelleri oluşturarak makinelerimizi (bilgisayarlarımızı) eğitmekle inşa edilir. Algoritma seçimi ise, ne tür verilere sahip olduğumuza ve ne tür bir görevi otomatikleştirmeye veya öngörü elde etmek istediğimize bağlı olarak değişiklik göstermektedir.
Makine öğreniminde verileri işlemek için temel olarak kullanılabilecek başlıca yaklaşımlar aşağıda yer almaktadır:
- Denetimli Makine Öğrenimi (Supervised Learning)
Denetimli makine öğrenimi yaklaşımı, kalıpları tanımlamak ve sonuçların nasıl ortaya çıktığını anlamak için bilinen girdi ve çıktıları kullanır. Verilen girdi kümesinden istenen çıktı kümesinin elde edilmesi için fonksiyon çözümlenir. Bilginin altında yatan mekanizmayı anlamak için model eğitimini ve veri setleri kullanır. Böylece, veri kümesinin bir parçası olmayan yeni bir girdi eklediğinizde, tanımlanan koşullarda hesaplanmış olası bir sonuç elde etmenizi sağlar.
Genellikle yapay zekaya odaklanan makine öğrenimi geliştiricileri, yazılım dış kaynak şirketleri, kimya, üretim ve pazarlama gibi çeşitli alanlarda denetimli makine öğrenimi kullanır.
- Denetimsiz Makine Öğrenimi (Unsupervised Learning)
Denetimsiz makine öğrenimi yaklaşımı, veri analizi için daha açıklayıcı bir yaklaşımdır. Model eğitimindeki veri ilişkilerini anlamak yerine, daha önce bilmediğiniz potansiyel kalıpları tespit etmek için etiketlenmemiş veriler kullanır. Bunu, benzer girdileri özelliklerine göre gruplayarak yapar.
Birçok mühendis bu yaklaşımı farklı endüstrilerde kullanır. Araştırma için özellikle yararlıdır, ancak satış departmanı, ürün fiyatlandırması veya lojistik için de kullanılabilir.
- Yarı Denetimli Makine Öğrenimi
Yarı denetimli makine öğrenme algoritmaları, denetimli ve denetimsiz öğrenme arasında bir yere düşer, çünkü eğitim için hem etiketli hem de etiketsiz verileri kullanırlar – tipik olarak az miktarda etiketlenmiş veri ve büyük miktarda etiketlenmemiş veri. Bu yöntemi kullanan sistemler öğrenme doğruluğunu önemli ölçüde geliştirebilir. Genellikle, yarı denetimli öğrenme, elde edilen etiketli veriler, onu eğitmek ve ondan öğrenmek için yetenekli ve ilgili kaynaklar gerektirdiğinde seçilir. Aksi takdirde, etiketlenmemiş verilerin edinilmesi genellikle ek kaynak gerektirmez.
- Pekiştirmeli Öğrenme
Pekiştirmeli makinesi öğrenme algoritmaları, eylemler üreterek çevresi ile etkileşime giren ve hataları veya ödülleri keşfeden bir öğrenme yöntemidir. Bu yöntem, makinelerin ve yazılım araçlarının performansını en üst düzeye çıkarmak için belirli bir bağlamdaki ideal davranışı otomatik olarak belirlemelerini sağlar.
Makine öğrenimi için daha birçok yaklaşım ve model bulunmaktadır. Klasik makine öğrenimi yaklaşımları ve modelleri üzerine daha detaylı bilgi edinmek istiyorsanız buraya göz atabilirsiniz.
Kuantum Makine Öğrenimi
Kuantum bilişim (kuantum bilgisayım) üzerine yaşanmakta olan gelişmeler makine öğrenimi ve yapay zeka alanlarında da etkisini göstermiştir. Ve muhtemelen şimdiye kadar kuantum bilişimin makine öğrenimi ve yapay zeka çözümlerini, veri setlerini klasik bilgisayım yöntemlerine göre daha hızlı hale getirme olasılığına sahip olduğunu tahmin etmişsinizdir. Klasik makine öğreniminde elde ettiğimiz sonuçların kuantum teknolojilerinin ve kuantum öğrenme algoritmalarının kullanımıyla, makine öğreniminin performansını artıp arttıramayacağı sorusu kuantum makine öğreniminin gelişimine sebebiyet vermiştir. Bunun sonucunda kuantum tabanlı makine öğrenimi kavramı ortaya çıkmıştır. Bu disiplinlerarası alan çok ama çok yenidir. Son çalışmalar ile birlikte, makine öğrenme yaklaşımıyla çalışan programların yapı taşları olarak işlev görebilecek kuantum algoritmaları üretilmektedir, ancak donanımsal ve yazılımsal zorlukları halen dikkate değer bir faktördür bu sistemlerin tasarımı klasik makine öğrenimi ve yapay zekadan oldukça farklıdır ve tamamen işlevsel kuantum bilgisayarların gelişimi halen daha bizlere çok uzaktır.
Kuantum makine öğrenimi, kuantum mekaniği ve makine öğreniminin kesiştiği noktada ortaya çıkan disiplinlerarası bir araştırma alanıdır. Kuantum algoritmalarının öngördüğü adımları analiz ederek, belirli problemler için klasik algoritmalara göre daha fazla performans potansiyeline sahip oldukları açıktır. Bu potansiyel kuantum hızlanması olarak bilinir. Kuantum mekaniğinden faydalanılan yöntemlerin, verilerde atipik paternler ürettiği iyi bilinmektedir. Küçük ölçekli kuantum bilgi işlemcileri, klasik bir bilgisayarın üretmesi için hesaplama açısından zor olan istatistiksel modeller üretebiliyorsa, belki de klasik olarak tanınması da aynı derecede zor olan desenleri tanıyabilir. Bu umudun gerçekleşmesi, makine öğrenimi için verimli kuantum algoritmalarının bulunup bulunamayacağına bağlıdır. Kuantum makine öğrenmesine yönelik algoritmaların birçoğu halen tamamen teorik olsa da ve test edilmesi için tam ölçekli bir evrensel kuantum bilgisayar gerektirse de, bazı algoritmalar küçük ölçekli veya özel amaçlı kuantum cihazlarına uygulanmıştır.
Terimsel olarak en yaygın kullanımı, bir kuantum bilgisayar üzerinde yürütülen klasik verilerin analizi için makine öğrenme algoritmalarını, yani kuantum-gelişmiş makine öğrenimini (quantum-enhanced machine learning) ifade eder. Klasik makine öğrenme algoritmaları muazzam miktarlarda verileri hesaplamak için kullanılırken, kuantum makine öğrenimi kuantum durumlar ve sistemler üzerinde analiz yapma fırsatları yaratarak bu alanın yeteneklerine farklı bir yönden katkı sunar. Bu, hesaplama açısından zor alt rutinlerin bir kuantum donanımına dış kaynak olarak sağlandığı hem klasik hem de kuantum bilgisayımını içeren hibrit yöntemleri içerir. Ayrıca, kuantum algoritmaları klasik veriler yerine kuantum durumlarını analiz etmek için de kullanılabilir.
Toparlayacak olursak, kuantum bilgisayım kullanarak klasik bir veri setinde bir makine öğrenme algoritması kurmak için, bu veri kümesini kuantum verilerine (kuantum durumları) dönüştürüp kuantum algoritmalarını uygulama işlemi, kuantum makine öğrenimini tanımlamaktadır.
Klasik makine öğrenimindeki bazı algoritmaların kuantum makine öğrenimi versiyonlarını buradan keşfedebilirsiniz.
Kuantum makine öğrenimi ile hızlandırılabilen mevcut makine öğrenimi süreçlerinden bazıları:
- Doğrusal Cebir: Doğrusal cebir hesaplamaları yapmak söz konusu olduğunda, kuantum bilgisayarlar beklentilerimizi arttırmamızı sağlayabilir. Bir kuantum geçidi, tek bir işlemde gelişmiş hızda eşit derecede üstel bir vektörle katlanan bir matris yürütebilir ve kuantum algoritmalarından makine öğrenme modelleri oluşturmaya yardımcı olur. Bu, doğrusal cebir hesaplamaları ile ilgili maliyetleri ve süreleri önemli ölçüde azaltır.
- Optimizasyon: Fizikçiler, kimyagerler ya da veri bilimcileri olsun, herkes yüksek boyutlu bir enerji ortamında en düşük enerji noktasına bir yol bulmaya çalışmaktadır. Adyabatik kuantum hesaplama ve kuantum tavlama dünyasında optimizasyon herkesin önceliğidir. Kuantum makine öğrenimi, fizikçilerin kuantum makine öğrenimi bağlamında denediği ilk görevlerden biri olan optimizasyonda güçlü bir ayak izine sahip olabilir.
- Çekirdek Değerlendirmesi: Kuantum makine öğrenimi, bir kuantum bilgisayardan tahminleri besleyerek çekirdek değerlendirmesini gerçekleştirmek için kullanılabilir, standart çekirdek yöntemine beslenebilir. Modelin eğitim ve çıkarımının destekli vektör makinesinde yapılması gerekecek olsa da, özel amaçlı kuantum destek vektör makinelerinin kullanılması süreci hızlandırmaya yardımcı olabilir. Özellik alanı genişledikçe, klasik hesaplamadaki çekirdek işlevlerinin hesaplanması pahalıya mal olur. Bu, kuantum algoritmalarının devreye girdiği yerdir. Dolanıklık ve girişim gibi kuantum özellikleri, çekirdek değerlendirmesini büyük ölçüde artırabilecek büyük bir kuantum durum alanı yaratmaya yardımcı olur.
- Derin Öğrenme: Derin öğrenme son zamanlarda makine öğrenimi ve yapay zekanın en etkili uygulamalarından biridir. Kuantum bilgisayarlar, klasik bilgisayarlarda çözülemeyen karmaşık sorunları çözerek derin öğrenmeyi çok daha ileriye taşıyabilir. Bir Boltzmann makinesini eğitmek için yapılan bir deneyde, Microsoft’tan araştırmacılar kuantum modelleri kullandılar ve sadece Boltzmann makinesini daha hızlı eğitmekle kalmayıp aynı zamanda klasik bir bilgisayarın verebileceğinden çok daha kapsamlı bir derin öğrenme çerçevesi elde edebildiklerini buldular.
Kuantum Veri
Kuantum veri, doğal veya yapay bir kuantum sisteminde meydana gelen herhangi bir veri kaynağıdır. Bu, Google’ın kuantum üstünlüğünü göstermesi için Sycamore işlemcisinden toplanan örnekler gibi bir kuantum bilgisayarı tarafından üretilen veriler olabilir. Kuantum verileri, üst üste binme ve dolanıklık sergiler, bu da temsil etmek veya depolamak için üstel miktarda klasik hesaplama kaynağı gerektirebilecek ortak olasılık dağılımlarına yol açar. Bir kübit bilgiyi klasik bir sistemden daha kompakt bir şekilde saklayabilir. Kuantum bilgileri sadece verilerin depolanma şeklini değiştirmekle kalmaz, aynı zamanda bu verileri nasıl manipüle ettiğimizi de etkiler. Kuantum veri esasında çok aşamalı veri hatları kullanmaktır.
Aşağıdakiler, bir kuantum cihazında oluşturulabilen veya simüle edilebilen kuantum verilerinin örnekleridir:
- Kimyasal simülasyon: Malzeme bilimi, hesaplamalı kimya, hesaplamalı biyoloji ve ilaç keşfi için potansiyel uygulamalarla kimyasal yapılar ve dinamikler hakkında bilgi edinimi.
- Kuantum madde simülasyonu: Çoklu kuantum etkileri sergileyen yüksek sıcaklık süper iletkenliği veya diğer egzotik halleri modellenebilir ve tasarlanabilir.
- Kuantum kontrolü: Hibrid kuantum-klasik modeller, optimum açık veya kapalı döngü kontrolü, kalibrasyon ve hata azaltma gerçekleştirmek için değişken olarak eğitilebilir. Bu, kuantum cihazları ve kuantum işlemciler için hata algılama ve düzeltme stratejilerini içerir.
- Kuantum iletişim ağları: Yapılandırılmış kuantum tekrarlayıcıları, kuantum alıcıları ve saflaştırma ünitelerinin tasarım ve yapımına yönelik uygulama ile, dikey olmayan kuantum durumları arasında ayrım yapmayı öğrenmek için makine öğrenimini kullanılabilir.
Kuantum Makine Öğrenimi Kütüphaneleri
Klasik makine öğreniminde olduğu gibi kuantum makine öğrenimi modelleri kurabilmek için özel olarak geliştirilmiş yazılım kütüphaneleri bulunmaktadır.
Başlıca kütüphaneler aşağıda listelenmiştir:
1. PennyLane
Fotonik tabanlı kuantum sistemler ve ileri yapay zeka alanında lider olan Xanadu, tarafından geliştirilen PennyLane kuantum bilgisayarlar için ilk özel makine öğrenimi kütüphanesidir. Çapraz platform desteği olan Penny Lane, açık kaynak kodludur. Python programlama dili ile geliştirilebilir. Dünya çapında geliştiricilerin, araştırmacıların ve meraklıların, kuantum makine öğreniminin en ileri alanında yer almasını sağlamayı hedeflemektedir. Ve şu an için en yaygın olarak kullanım bulan kuantum makine öğrenimi kütüphanesidir. Kütüphane, şu anda mevcut olan API’ler ve alandaki en büyük oyuncuların kuantum donanımı ile sorunsuz bir şekilde entegre edilebilirdir.
Kütüphane, hem kübit hem de sürekli değişken paradigmaları destekleyen kuantum cihazları için birleşik bir mimari sağlar. PennyLane’nin temel özelliği, varyasyonlu kuantum devrelerinin gradyanlarını, geri yayılım gibi klasik tekniklerle uyumlu bir şekilde hesaplama yeteneğidir. Böylece PennyLane, optimizasyon ve makine öğreniminde yaygın olan otomatik farklılaştırma algoritmalarını kuantum ve hibrit hesaplamaları içerecek şekilde genişletir. Eklentileri, sistemi çerçeveyi kapı tabanlı herhangi bir kuantum simülatörü veya donanımı ile uyumlu hale getirir. Bu yol ile, PennyLane optimizasyonlarının Rigetti ve IBM Q tarafından sağlanan halka açık kuantum cihazlarında çalıştırılmasına izin verilmektedir. Diğer bir yönden PennyLane, varyasyonel kuantum özdeğer, özvektör, özuzay optimizasyonu, kuantum yaklaşık optimizasyon, kuantum makine öğrenme modelleri ve diğer birçok uygulama için kullanılabilir.
TensorFlow ve PyTorch gibi kütüphaneler, güçlü GPU donanımına bir arabirim sağlayarak yapay zeka konusundaki gelişmelerin önünü açmıştır. Xanadu’da bu kütüphane ile kuantum donanımlarına yönelik makine öğrenimi çalışmalarının önünü açmayı planlamaktadır.
İnternet Sitesi: https://pennylane.ai/
2. Tensorflow Quantum
TensorFlow Quantum (TFQ), hibrit kuantum-klasik makine öğrenimi modellerinin hızlı prototiplenmesi için bir kuantum makine öğrenimi kütüphanesidir. Kuantum algoritmaları ve uygulamalarıyla yapılan ilgili araştırmalarda TensorFlow içindeki Google’ın kuantum hesaplama çerçevelerinden yararlanabilir. TensorFlow Quantum’un ana fikri, TensorFlow programlama çerçevesi üzerinde kuantum algoritmalarını ve makine öğrenimi sistemlerini birleştirmektir. Google, bu yaklaşımı kuantum makine öğrenimi olarak ifade eder ve Google Cirq gibi son zamanlardaki kuantum hesaplama çerçevelerinden bazılarını kullanarak uygulayabilir.
Tensorfow Quantum hakkında aşağıdaki yazımızdan bilgi edinebilirsiniz.
İnternet Sitesi: https://www.tensorflow.org/quantum/
3. Qiskit
Qiskit, kuantum bilgisayım için açık kaynaklı bir çerçevedir. Kuantum programları oluşturmak ve değiştirmek ve bunları IBM Q Experience’daki prototip kuantum cihazlarında veya yerel bir bilgisayardaki simülatörlerde çalıştırmak için araçlar sağlar. PyTorch ve Tensorflow ile birlikte kullanım imkanı tanıyarak makine öğrenimi ve yapay zeka çalışmalarına izin vermektedir.
Detaylı bilgi için: Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
İnternet Sitesi: https://qiskit.org/
4. Microsoft QDK Quantum ML
28 Şubat 2020’de duyurdukları yeni güncelleme ile Microsoft’un Quantum Development Kit’i kullanılarak kuantum makine öğrenimi modelleri kurulabilmektedir. Quantum ML kütüphanesi, denetimli sınıflandırma problemlerini çözmek için yeni bir hibrit kuantum-klasik algoritma olan devre merkezli kuantum sınıflandırıcıyı uygulamak için gerekli araçları sağlar. Ayrıca, devre merkezli kuantum sınıflandırıcısını kullanarak verileri sınıflandırmak için de kullanılabilir.
Detaylı bilgi için: New Quantum Machine Learning library for the QDK
İnternet Sitesi: https://microsoft.com/en-us/quantum/development-kit
Diğer Kuantum Makine Öğrenimi Kütüphaneleri
Faydalı Olabilecek Derlemeler
- Awesome Quantum Machine Learning
- Awesome Quantum ML
- Quantum Learning
- Quantum Machine Learning algorithms with Qiskit
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